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Anfangs und Endzustand jeder Stunden berücksichtigt.
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259
test.py
259
test.py
@@ -23,7 +23,7 @@ import os
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start_hour = 11
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start_hour = 13
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pv_forecast= [
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0,
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@@ -325,262 +325,11 @@ start_solution= [
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1,
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1
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]
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parameter= {"preis_euro_pro_wh_akku": 30e-05,'pv_soc': 60.4052, 'pv_akku_cap': 30000, 'year_energy': 4100000, 'einspeiseverguetung_euro_pro_wh': 7e-05, 'max_heizleistung': 1000,"gesamtlast":gesamtlast, 'pv_forecast': pv_forecast, "temperature_forecast":temperature_forecast, "strompreis_euro_pro_wh":strompreis_euro_pro_wh, 'eauto_min_soc': 80, 'eauto_cap': 60000, 'eauto_charge_efficiency': 0.95, 'eauto_charge_power': 7590, 'eauto_soc': 53, 'pvpowernow': 211.137503624, 'start_solution': start_solution, 'haushaltsgeraet_wh': 937, 'haushaltsgeraet_dauer': 0}
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parameter= {"preis_euro_pro_wh_akku": 30e-05,'pv_soc': 95.00, 'pv_akku_cap': 30000, 'year_energy': 4100000, 'einspeiseverguetung_euro_pro_wh': 7e-05, 'max_heizleistung': 1000,"gesamtlast":gesamtlast, 'pv_forecast': pv_forecast, "temperature_forecast":temperature_forecast, "strompreis_euro_pro_wh":strompreis_euro_pro_wh, 'eauto_min_soc': 0, 'eauto_cap': 60000, 'eauto_charge_efficiency': 0.95, 'eauto_charge_power': 7590, 'eauto_soc': 53, 'pvpowernow': 211.137503624, 'start_solution': start_solution, 'haushaltsgeraet_wh': 937, 'haushaltsgeraet_dauer': 0}
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opt_class = optimization_problem(prediction_hours=48, strafe=10,optimization_hours=24)
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ergebnis = opt_class.optimierung_ems(parameter=parameter, start_hour=start_hour)
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# #Gesamtlast
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# #############
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# gesamtlast = Gesamtlast()
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# # Load Forecast
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# ###############
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# lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
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# #leistung_haushalt = lf.get_daily_stats(date)[0,...] # Datum anpassen
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# leistung_haushalt = lf.get_stats_for_date_range(date_now,date)[0,...].flatten()
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# # print(date_now," ",date)
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# # print(leistung_haushalt.shape)
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# gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
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# # PV Forecast
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# ###############
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# #PVforecast = PVForecast(filepath=os.path.join(r'test_data', r'pvprognose.json'))
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# PVforecast = PVForecast(prediction_hours = prediction_hours, url="https://api.akkudoktor.net/forecast?lat=50.8588&lon=7.3747&power=5000&azimuth=-10&tilt=7&powerInvertor=10000&horizont=20,27,22,20&power=4800&azimuth=-90&tilt=7&powerInvertor=10000&horizont=30,30,30,50&power=1400&azimuth=-40&tilt=60&powerInvertor=2000&horizont=60,30,0,30&power=1600&azimuth=5&tilt=45&powerInvertor=1400&horizont=45,25,30,60&past_days=5&cellCoEff=-0.36&inverterEfficiency=0.8&albedo=0.25&timezone=Europe%2FBerlin&hourly=relativehumidity_2m%2Cwindspeed_10m")
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# pv_forecast = PVforecast.get_pv_forecast_for_date_range(date_now,date) #get_forecast_for_date(date)
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# temperature_forecast = PVforecast.get_temperature_for_date_range(date_now,date)
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# # Strompreise
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# ###############
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# filepath = os.path.join (r'test_data', r'strompreise_akkudokAPI.json') # Pfad zur JSON-Datei anpassen
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# #price_forecast = HourlyElectricityPriceForecast(source=filepath)
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# price_forecast = HourlyElectricityPriceForecast(source="https://api.akkudoktor.net/prices?start="+date_now+"&end="+date+"")
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# specific_date_prices = price_forecast.get_price_for_daterange(date_now,date)
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# # print("13:",specific_date_prices[13])
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# # print("14:",specific_date_prices[14])
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# # print("15:",specific_date_prices[15])
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# # sys.exit()
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# # WP
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# ##############
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# leistung_wp = wp.simulate_24h(temperature_forecast)
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# gesamtlast.hinzufuegen("Heatpump", leistung_wp)
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# # EAuto
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# ######################
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# # leistung_eauto = eauto.get_stuendliche_last()
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# # soc_eauto = eauto.get_stuendlicher_soc()
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# # gesamtlast.hinzufuegen("eauto", leistung_eauto)
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# # print(gesamtlast.gesamtlast_berechnen())
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# # EMS / Stromzähler Bilanz
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# #akku=None, pv_prognose_wh=None, strompreis_cent_pro_wh=None, einspeiseverguetung_cent_pro_wh=None, eauto=None, gesamtlast=None
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# ems = EnergieManagementSystem(akku=akku, gesamtlast = gesamtlast, pv_prognose_wh=pv_forecast, strompreis_cent_pro_wh=specific_date_prices, einspeiseverguetung_cent_pro_wh=einspeiseverguetung_cent_pro_wh, eauto=eauto)
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# o = ems.simuliere(start_hour)#ems.simuliere_ab_jetzt()
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# #pprint(o)
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# #pprint(o["Gesamtbilanz_Euro"])
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# #visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,discharge_array,laden_moeglich, temperature_forecast, start_hour, prediction_hours)
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# # Optimierung
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# def evaluate_inner(individual):
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# #print(individual)
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# discharge_hours_bin = individual[0::2]
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# eautocharge_hours_float = individual[1::2]
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# #print(discharge_hours_bin)
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# #print(len(eautocharge_hours_float))
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# ems.reset()
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# #eauto.reset()
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# ems.set_akku_discharge_hours(discharge_hours_bin)
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# ems.set_eauto_charge_hours(eautocharge_hours_float)
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# #eauto.set_laden_moeglich(eautocharge_hours_float)
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# #eauto.berechne_ladevorgang()
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# #leistung_eauto = eauto.get_stuendliche_last()
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# #gesamtlast.hinzufuegen("eauto", leistung_eauto)
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# #ems.set_gesamtlast(gesamtlast.gesamtlast_berechnen())
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# o = ems.simuliere(start_hour)
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# return o, eauto
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# # Fitness-Funktion (muss Ihre EnergieManagementSystem-Logik integrieren)
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# def evaluate(individual):
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# o,eauto = evaluate_inner(individual)
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# gesamtbilanz = o["Gesamtbilanz_Euro"]
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# # Überprüfung, ob der Mindest-SoC erreicht wird
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# final_soc = eauto.ladezustand_in_prozent() # Nimmt den SoC am Ende des Optimierungszeitraums
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# strafe = 0.0
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# #if final_soc < min_soc_eauto:
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# # Fügt eine Strafe hinzu, wenn der Mindest-SoC nicht erreicht wird
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# strafe = max(0,(min_soc_eauto - final_soc) * hohe_strafe ) # `hohe_strafe` ist ein vorher festgelegter Strafwert
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# gesamtbilanz += strafe
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# gesamtbilanz += o["Gesamt_Verluste"]/1000.0
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# return (gesamtbilanz,)
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# # Werkzeug-Setup
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# creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
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# creator.create("Individual", list, fitness=creator.FitnessMin)
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# toolbox = base.Toolbox()
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# toolbox.register("attr_bool", random.randint, 0, 1)
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# toolbox.register("attr_bool", random.randint, 0, 1)
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# toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_bool,toolbox.attr_bool), n=prediction_hours)
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# toolbox.register("population", tools.initRepeat, list, toolbox.individual)
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# toolbox.register("evaluate", evaluate)
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# toolbox.register("mate", tools.cxTwoPoint)
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# toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
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# toolbox.register("select", tools.selTournament, tournsize=3)
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# # Genetischer Algorithmus
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# def optimize():
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# population = toolbox.population(n=500)
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# hof = tools.HallOfFame(1)
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# stats = tools.Statistics(lambda ind: ind.fitness.values)
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# stats.register("avg", np.mean)
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# stats.register("min", np.min)
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# stats.register("max", np.max)
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# algorithms.eaMuPlusLambda(population, toolbox, 50, 100, cxpb=0.5, mutpb=0.5, ngen=500, stats=stats, halloffame=hof, verbose=True)
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# #algorithms.eaSimple(population, toolbox, cxpb=0.2, mutpb=0.2, ngen=1000, stats=stats, halloffame=hof, verbose=True)
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# return hof[0]
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# start_solution = optimize()
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# print("Start Lösung:", start_solution)
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# # # Werkzeug-Setup
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# # creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
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# # creator.create("Individual", list, fitness=creator.FitnessMin)
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# # toolbox = base.Toolbox()
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# # toolbox.register("attr_bool", random.randint, 0, 1)
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# # toolbox.register("attr_float", random.uniform, 0.0, 1.0)
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# # toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_bool,toolbox.attr_float), n=prediction_hours)
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# # start_individual = toolbox.individual()
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# # start_individual[:] = start_solution
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# # toolbox.register("population", tools.initRepeat, list, toolbox.individual)
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# # toolbox.register("evaluate", evaluate)
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# # toolbox.register("mate", tools.cxTwoPoint)
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# # toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
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# # toolbox.register("select", tools.selTournament, tournsize=3)
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# # # Genetischer Algorithmus
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# # def optimize():
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# # population = toolbox.population(n=1000)
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# # population[0] = start_individual
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# # hof = tools.HallOfFame(1)
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# # stats = tools.Statistics(lambda ind: ind.fitness.values)
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# # stats.register("avg", np.mean)
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# # stats.register("min", np.min)
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# # stats.register("max", np.max)
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# # algorithms.eaMuPlusLambda(population, toolbox, 100, 200, cxpb=0.5, mutpb=0.2, ngen=1000, stats=stats, halloffame=hof, verbose=True)
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# # #algorithms.eaSimple(population, toolbox, cxpb=0.2, mutpb=0.2, ngen=1000, stats=stats, halloffame=hof, verbose=True)
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# # return hof[0]
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# # best_solution = optimize()
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# best_solution = start_solution
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# print("Beste Lösung:", best_solution)
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# #ems.set_akku_discharge_hours(best_solution)
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# o,eauto = evaluate_inner(best_solution)
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# # soc_eauto = eauto.get_stuendlicher_soc()
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# # print(soc_eauto)
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# # pprint(o)
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# # pprint(eauto.get_stuendlicher_soc())
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# #visualisiere_ergebnisse(gesamtlast,leistung_haushalt,leistung_wp, pv_forecast, specific_date_prices, o,soc_eauto,best_solution[0::2],best_solution[1::2] , temperature_forecast)
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# visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,best_solution[0::2],best_solution[1::2] , temperature_forecast, start_hour, prediction_hours)
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# # for data in forecast.get_forecast_data():
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# # print(data.get_date_time(), data.get_dc_power(), data.get_ac_power(), data.get_windspeed_10m(), data.get_temperature())for data in forecast.get_forecast_data():
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# # app = Flask(__name__)
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# # @app.route('/getdata', methods=['GET'])
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# # def get_data():
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# # # Hole das Datum aus den Query-Parametern
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# # date_str = request.args.get('date')
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# # year_energy = request.args.get('year_energy')
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# # try:
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# # # Konvertiere das Datum in ein datetime-Objekt
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# # date_obj = datetime.strptime(date_str, '%Y-%m-%d')
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# # filepath = r'.\load_profiles.npz' # Pfad zur JSON-Datei anpassen
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# # lf = cl.LoadForecast(filepath=filepath, year_energy=float(year_energy))
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# # specific_date_prices = lf.get_daily_stats('2024-02-16')
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# # # Berechne den Tag des Jahres
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# # #day_of_year = date_obj.timetuple().tm_yday
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# # # Konvertiere den Tag des Jahres in einen String, falls die Schlüssel als Strings gespeichert sind
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# # #day_key = int(day_of_year)
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# # #print(day_key)
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# # # Überprüfe, ob der Tag im Jahr in den Daten vorhanden ist
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# # array_list = lf.get_daily_stats(date_str)
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# # pprint(array_list)
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# # pprint(array_list.shape)
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# # if array_list.shape == (2,24):
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# # #if day_key < len(load_profiles_exp):
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# # # Konvertiere das Array in eine Liste für die JSON-Antwort
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# # #((load_profiles_exp_l[day_key]).tolist(),(load_profiles_std_l)[day_key].tolist())
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# # return jsonify({date_str: array_list.tolist()})
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# # else:
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# # return jsonify({"error": "Datum nicht gefunden"}), 404
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# # except ValueError:
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# # # Wenn das Datum nicht im richtigen Format ist oder ungültig ist
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# # return jsonify({"error": "Ungültiges Datum"}), 400
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# # if __name__ == '__main__':
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# # app.run(debug=True)
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print(ergebnis)
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print(jsonify(ergebnis))
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